Some settings
Mexico
Merge and filter those without lat and lon, then search with
osm.
## # A tibble: 114 × 5
## ent med mun loc n
## <chr> <chr> <chr> <chr> <int>
## 1 campeche c.s jóse ma. morelos y pavón o cibalito <NA> <NA> 2
## 2 campeche caravana médico dental p-43 <NA> <NA> 1
## 3 campeche chan laguna p-31 <NA> <NA> 1
## 4 campeche manuel crecencio rejon p-32 <NA> <NA> 2
## 5 campeche médico dental p-43 <NA> <NA> 1
## 6 campeche médico dental p-50 <NA> <NA> 2
## 7 campeche santa rosa p-30 <NA> <NA> 2
## 8 campeche tomás aznar barbachano p-28 <NA> <NA> 2
## 9 campeche unidad médica móvil p-32 <NA> <NA> 1
## 10 campeche valentin gomez farias p-31 <NA> <NA> 1
## # … with 104 more rows
ENT and MED are matched but not LOC
There are 0 obs. with missing lon or lat.
## # A tibble: 2 × 2
## n nn
## <int> <int>
## 1 1 25797
## 2 2 2762
Only animal and pep are on fine-scale
## [1] 1612
OK, if get all these filled for all hexagons maybe we can predict a little.